Current AI-assisted protein design mainly utilizes protein sequential and
structural information. Meanwhile, there exists tremendous knowledge curated by
humans in the text format describing proteins' high-level properties. Yet,
whether the incorporation of such text data can help protein design tasks has
not been explored. To bridge this gap, we propose ProteinDT, a multi-modal
framework that leverages textual descriptions for protein design. ProteinDT
consists of three subsequent steps: ProteinCLAP that aligns the representation
of two modalities, a facilitator that generates the protein representation from
the text modality, and a decoder that generates the protein sequences from the
representation. To train ProteinDT, we construct a large dataset,
SwissProtCLAP, with 441K text and protein pairs. We empirically verify the
effectiveness of ProteinDT from three aspects: (1) consistently superior
performance on four out of six protein property prediction benchmarks; (2) over
90% accuracy for text-guided protein generation; and (3) promising results for
zero-shot text-guided protein editing.

提出了一种多模态框架 ProteinDT，旨在改进蛋白设计。通过结合文本信息和蛋白表征，该框架在蛋白性质预测和生成方面取得了令人满意的结果，并构建了一个大型数据集 SwissProtCLAP 来训练模型。